import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__A    )
class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   field(default='''text-classification'''   ,			metadata={'''include_in_asdict_even_if_is_default''': True}    )
				lowerCamelCase_        =   Features({'''text''': Value('''string'''    )}    )
				lowerCamelCase_        =   Features({'''labels''': ClassLabel}    )
				lowerCamelCase_        =   "text"
				lowerCamelCase_        =   "labels"
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						if self.label_column not in features:
								raise ValueError(F'''Column {self.label_column} is not present in features.'''   )
						if not isinstance(features[self.label_column]						,      lowercase   ):
								raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''   )
						A_						:							int      = copy.deepcopy(self   )
						A_						:							int      = self.label_schema.copy()
						A_						:							Dict      = features[self.label_column]
						A_						:							List[Any]      = label_schema
						return task_template







				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return {
						    self.text_column: "text",
						    self.label_column: "labels",
						}







import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_UpperCAmelCase          =						collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_UpperCAmelCase          =						"""https://storage.googleapis.com/cvdf-datasets/mnist/"""




def 							UpperCamelCase		(			__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							Tuple      = numpy.dtype(numpy.uintaa      ).newbyteorder('>'      )
		return numpy.frombuffer(bytestream.read(4      )		,dtype=__lowercase      )[0]




@deprecated(__lowercase		,'Please use tf.data to implement this functionality.'      )
def 							UpperCamelCase		(			__lowercase		:					List[str]      ):
		'''simple docstring'''




		print('Extracting'		,f.name      )
		with gzip.GzipFile(fileobj=__lowercase      ) as bytestream:
				A_						:							str      = _readaa(__lowercase      )
				if magic != 20_51:
						raise ValueError(
						    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)      )
				A_						:							Optional[int]      = _readaa(__lowercase      )
				A_						:							List[Any]      = _readaa(__lowercase      )
				A_						:							Any      = _readaa(__lowercase      )
				A_						:							List[str]      = bytestream.read(rows * cols * num_images      )
				A_						:							Optional[int]      = numpy.frombuffer(__lowercase		,dtype=numpy.uinta      )
				A_						:							Union[str, Any]      = data.reshape(__lowercase		,__lowercase		,__lowercase		,1      )
				return data




@deprecated(__lowercase		,'Please use tf.one_hot on tensors.'      )
def 							UpperCamelCase		(			__lowercase		:					Optional[int]		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							Dict      = labels_dense.shape[0]
		A_						:							List[str]      = numpy.arange(__lowercase      ) * num_classes
		A_						:							Any      = numpy.zeros((num_labels, num_classes)      )
		A_						:							List[Any]      = 1
		return labels_one_hot




@deprecated(__lowercase		,'Please use tf.data to implement this functionality.'      )
def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					Tuple=False		,__lowercase		:					List[str]=10      ):
		'''simple docstring'''




		print('Extracting'		,f.name      )
		with gzip.GzipFile(fileobj=__lowercase      ) as bytestream:
				A_						:							int      = _readaa(__lowercase      )
				if magic != 20_49:
						raise ValueError(
						    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)      )
				A_						:							List[Any]      = _readaa(__lowercase      )
				A_						:							Dict      = bytestream.read(__lowercase      )
				A_						:							List[str]      = numpy.frombuffer(__lowercase		,dtype=numpy.uinta      )
				if one_hot:
						return _dense_to_one_hot(__lowercase		,__lowercase      )
				return labels





class UpperCAmelCase		:


				'''simple docstring'''






				@deprecated(
				    lowercase						,      'Please use alternatives such as official/mnist/_DataSet.py'
				    ' from tensorflow/models.'						,      )
				def __init__(		self						,      lowercase						,      lowercase						,      lowercase=False						,      lowercase=False						,      lowercase=dtypes.floataa						,      lowercase=True						,      lowercase=None						,      ):



						"""simple docstring"""





						A_  ,     A_						:							Dict      = random_seed.get_seed(lowercase   )
						# If op level seed is not set, use whatever graph level seed is returned
						numpy.random.seed(seeda if seed is None else seeda   )
						A_						:							Optional[int]      = dtypes.as_dtype(lowercase   ).base_dtype
						if dtype not in (dtypes.uinta, dtypes.floataa):
								raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype   )
						if fake_data:
								A_						:							Optional[int]      = 1_0_0_0_0
								A_						:							int      = one_hot
						else:
								assert (
								    images.shape[0] == labels.shape[0]
								), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
								A_						:							Dict      = images.shape[0]

								# Convert shape from [num examples, rows, columns, depth]
								# to [num examples, rows*columns] (assuming depth == 1)
								if reshape:
										assert images.shape[3] == 1
										A_						:							int      = images.reshape(
										    images.shape[0]						,      images.shape[1] * images.shape[2]   )
								if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										A_						:							Dict      = images.astype(numpy.floataa   )
										A_						:							Optional[int]      = numpy.multiply(lowercase						,      1.0 / 255.0   )
						A_						:							Optional[int]      = images
						A_						:							List[Any]      = labels
						A_						:							str      = 0
						A_						:							Union[str, Any]      = 0
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._images
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._labels
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._num_examples
				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return self._epochs_completed







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=False						,      lowercase=True   ):



						"""simple docstring"""





						if fake_data:
								A_						:							List[Any]      = [1] * 7_8_4
								A_						:							Dict      = [1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(lowercase   )],
								    [fake_label for _ in range(lowercase   )],
								)
						A_						:							int      = self._index_in_epoch
						# Shuffle for the first epoch
						if self._epochs_completed == 0 and start == 0 and shuffle:
								A_						:							Tuple      = numpy.arange(self._num_examples   )
								numpy.random.shuffle(lowercase   )
								A_						:							Dict      = self.images[perma]
								A_						:							Optional[Any]      = self.labels[perma]
						# Go to the next epoch
						if start + batch_size > self._num_examples:
								# Finished epoch
								self._epochs_completed += 1
								# Get the rest examples in this epoch
								A_						:							List[Any]      = self._num_examples - start
								A_						:							Optional[int]      = self._images[start : self._num_examples]
								A_						:							Optional[int]      = self._labels[start : self._num_examples]
								# Shuffle the data
								if shuffle:
										A_						:							Tuple      = numpy.arange(self._num_examples   )
										numpy.random.shuffle(lowercase   )
										A_						:							Dict      = self.images[perm]
										A_						:							Any      = self.labels[perm]
								# Start next epoch
								A_						:							Any      = 0
								A_						:							Optional[Any]      = batch_size - rest_num_examples
								A_						:							Optional[int]      = self._index_in_epoch
								A_						:							Dict      = self._images[start:end]
								A_						:							Any      = self._labels[start:end]
								return (
								    numpy.concatenate((images_rest_part, images_new_part)						,      axis=0   ),
								    numpy.concatenate((labels_rest_part, labels_new_part)						,      axis=0   ),
								)
						else:
								self._index_in_epoch += batch_size
								A_						:							Tuple      = self._index_in_epoch
								return self._images[start:end], self._labels[start:end]




@deprecated(__lowercase		,'Please write your own downloading logic.'      )
def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					Optional[int]		,__lowercase		:					Any      ):
		'''simple docstring'''




		if not gfile.Exists(__lowercase      ):
				gfile.MakeDirs(__lowercase      )
		A_						:							Optional[Any]      = os.path.join(__lowercase		,__lowercase      )
		if not gfile.Exists(__lowercase      ):
				urllib.request.urlretrieve(__lowercase		,__lowercase      )  # noqa: S310
				with gfile.GFile(__lowercase      ) as f:
						A_						:							Optional[int]      = f.size()
				print('Successfully downloaded'		,__lowercase		,__lowercase		,'bytes.'      )
		return filepath




@deprecated(
    __lowercase		,'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'      )
def 							UpperCamelCase		(			__lowercase		:					Dict		,__lowercase		:					str=False		,__lowercase		:					int=False		,__lowercase		:					str=dtypes.floataa		,__lowercase		:					int=True		,__lowercase		:					List[Any]=50_00		,__lowercase		:					Union[str, Any]=None		,__lowercase		:					Union[str, Any]=DEFAULT_SOURCE_URL		,):
		'''simple docstring'''




		if fake_data:

				def fake():
						return _DataSet(
						    []		,[]		,fake_data=__lowercase		,one_hot=__lowercase		,dtype=__lowercase		,seed=__lowercase      )

				A_						:							Union[str, Any]      = fake()
				A_						:							Any      = fake()
				A_						:							str      = fake()
				return _Datasets(train=__lowercase		,validation=__lowercase		,test=__lowercase      )

		if not source_url:  # empty string check
				A_						:							Tuple      = DEFAULT_SOURCE_URL

		A_						:							List[Any]      = 'train-images-idx3-ubyte.gz'
		A_						:							Optional[int]      = 'train-labels-idx1-ubyte.gz'
		A_						:							Optional[Any]      = 't10k-images-idx3-ubyte.gz'
		A_						:							Optional[int]      = 't10k-labels-idx1-ubyte.gz'

		A_						:							str      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + train_images_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							Optional[int]      = _extract_images(__lowercase      )

		A_						:							Optional[Any]      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + train_labels_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							Union[str, Any]      = _extract_labels(__lowercase		,one_hot=__lowercase      )

		A_						:							Any      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + test_images_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							str      = _extract_images(__lowercase      )

		A_						:							Union[str, Any]      = _maybe_download(
		    __lowercase		,__lowercase		,source_url + test_labels_file      )
		with gfile.Open(__lowercase		,'rb'      ) as f:
				A_						:							int      = _extract_labels(__lowercase		,one_hot=__lowercase      )

		if not 0 <= validation_size <= len(__lowercase      ):
				A_						:							Dict      = (
				    'Validation size should be between 0 and '
				    f'''{len(__lowercase      )}. Received: {validation_size}.'''
				)
				raise ValueError(__lowercase      )

		A_						:							int      = train_images[:validation_size]
		A_						:							List[str]      = train_labels[:validation_size]
		A_						:							Tuple      = train_images[validation_size:]
		A_						:							Dict      = train_labels[validation_size:]

		A_						:							str      = {'dtype': dtype, 'reshape': reshape, 'seed': seed}

		A_						:							Optional[int]      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )
		A_						:							int      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )
		A_						:							int      = _DataSet(__lowercase		,__lowercase		,**__lowercase      )

		return _Datasets(train=__lowercase		,validation=__lowercase		,test=__lowercase      )






